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Recently, Large Vision-Language Models (LVLMs) have demonstrated impressive capabilities in multi-modal context comprehension.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014 · 2014
Earlier work this paper cites.
Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. 2017 · 2017
Earlier work this paper cites.
Gqa: A new dataset for real-world visual reasoning and compositional question answering
Drew A Hudson and Christopher D Manning. 2019 · 2019
Earlier work this paper cites.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021 · 2021
Earlier work this paper cites.
Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. 2022 · 2022
Earlier work this paper cites.
A-okvqa: A benchmark for visual question answering using world knowledge
Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, Kenneth Marino, and Roozbeh Mottaghi. 2022 · 2022
Earlier work this paper cites.
Vision–language model for visual question answering in medical imagery
Yakoub Bazi, Mohamad Mahmoud Al Rahhal, Laila Bashmal, and Mansour Zuair. 2023 · 2023
Earlier work this paper cites.
Mitigating hallucination in visual language models with visual supervision
Zhiyang Chen, Yousong Zhu, Yufei Zhan, Zhaowen Li, Chaoyang Zhao, Jinqiao Wang, and Ming Tang. 2023 · 2023
Earlier work this paper cites.
Qidong Huang, Xiaoyi Dong, Pan Zhang, Bin Wang, Conghui He, Jiaqi Wang, Dahua Lin, Weiming Zhang, and Nenghai Yu. 2023 · 2023
Earlier work this paper cites.
Mitigating object hallucinations in large vision-language models through visual contrastive decoding
Sicong Leng, Hang Zhang, Guanzheng Chen, Xin Li, Shijian Lu, Chunyan Miao, and Lidong Bing. 2023 · 2023
Cited alongside, same era.
Lm-nav: Robotic navigation with large pre-trained models of language, vision, and action
Dhruv Shah, Błażej Osiński, Sergey Levine, et al. 2023 · 2023
Cited alongside, same era.
Aligning large multimodal models with factually augmented rlhf
Zhiqing Sun, Sheng Shen, Shengcao Cao, Haotian Liu, Chunyuan Li, Yikang Shen, Chuang Gan, Liang-Yan Gui, Yu-Xiong Wang, Yiming Yang, et al. 2023 · 2023
Cited alongside, same era.
Woodpecker: Hallucination correction for multimodal large language models
Shukang Yin, Chaoyou Fu, Sirui Zhao, Tong Xu, Hao Wang, Dianbo Sui, Yunhang Shen, Ke Li, Xing Sun, and Enhong Chen. 2023 · 2023
Cited alongside, same era.
Visiongpt: Vision-language understanding agent using generalized multimodal framework
Chris Kelly, Luhui Hu, Bang Yang, Yu Tian, Deshun Yang, Cindy Yang, Zaoshan Huang, Zihao Li, Jiayin Hu, and Yuexian Zou. 2024 · 2024
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Llava-med: Training a large language-and-vision assistant for biomedicine in one day
Chunyuan Li, Cliff Wong, Sheng Zhang, Naoto Usuyama, Haotian Liu, Jianwei Yang, Tristan Naumann, Hoifung Poon, and Jianfeng Gao. 2024 · 2024
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Twin-merging: Dynamic integration of modular expertise in model merging
Zhenyi Lu, Chenghao Fan, Wei Wei, Xiaoye Qu, Dangyang Chen, and Yu Cheng. 2024 · 2024
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Conflictbank: A benchmark for evaluating the influence of knowledge conflicts in llm
Zhaochen Su, Jun Zhang, Xiaoye Qu, Tong Zhu, Yanshu Li, Jiashuo Sun, Juntao Li, Min Zhang, and Yu Cheng. 2024 · 2024
Closest in time.
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Tianyu Yu, Yuan Yao, Haoye Zhang, Taiwen He, Yifeng Han, Ganqu Cui, Jinyi Hu, Zhiyuan Liu, Hai-Tao Zheng, Maosong Sun, et al. 2023 · 2023
Cited alongside, same era.
Beyond hallucinations: Enhancing lvlms through hallucination-aware direct preference optimization
Zhiyuan Zhao, Bin Wang, Linke Ouyang, Xiaoyi Dong, Jiaqi Wang, and Conghui He. 2023 · 2023
Cited alongside, same era.
A survey on multimodal large language models for autonomous driving
Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Yang Zhou, Kaizhao Liang, Jintai Chen, Juanwu Lu, Zichong Yang, Kuei-Da Liao, et al. 2024 · 2024
Cited alongside, same era.
Mme: A comprehensive evaluation benchmark for multimodal large language models
Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, Yunsheng Wu, and Rongrong Ji. 2024 · 2024
Cited alongside, same era.
Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. 2023a
Cited in the paper.
Evaluating object hallucination in large vision-language models
Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen. 2023b
Cited in the paper.
A survey of attacks on large vision-language models: Resources, advances, and future trends
Daizong Liu, Mingyu Yang, Xiaoye Qu, Pan Zhou, Wei Hu, and Yu Cheng. 2024a
Cited in the paper.
Mitigating hallucination in large multi-modal models via robust instruction tuning
Fuxiao Liu, Kevin Lin, Linjie Li, Jianfeng Wang, Yaser Yacoob, and Lijuan Wang. 2023a
Cited in the paper.
Xiaoyu Tian, Junru Gu, Bailin Li, Yicheng Liu, Chenxu Hu, Yang Wang, Kun Zhan, Peng Jia, Xianpeng Lang, and Hang Zhao. 2024 · 2024
Closest in time.
Vigor: Improving visual grounding of large vision language models with fine-grained reward modeling
Siming Yan, Min Bai, Weifeng Chen, Xiong Zhou, Qixing Huang, and Li Erran Li. 2024 · 2024
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Avoiding feature suppression in contrastive learning: Learning what has not been learned before
Jihai Zhang, Xiang Lan, Xiaoye Qu, Yu Cheng, Mengling Feng, and Bryan Hooi. 2024 · 2024
Closest in time.
Mitigating object hallucination in large vision-language models via classifier-free guidance
Linxi Zhao, Yihe Deng, Weitong Zhang, and Quanquan Gu. 2024 · 2024
Closest in time.